R Notebook

This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter.

source("./tianfengRwrappers.R")

# plan("multiprocess",workers = 8)

读取结果

human_coronary <- readRDS("human_coronary.rds")
CA_dataset1 <- readRDS("CA_dataset1.rds")
CA_dataset2 <- readRDS("CA_dataset2.rds") #已经经过分组处理了

修改分群

基质细胞分类

ECs亚群分析 整合

整合算法可能出现负值，运行SCENIC时舍弃了这些异常值

# 提取内皮细胞亚群
ECs_list <- list(subset(CA_dataset1, idents = "Endothelial"), subset(human_coronary, idents = "Endothelial"))

ECs_list <- lapply(X = ECs_list, FUN = function(x) {
  x <- NormalizeData(x)
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# 需要分析的差异基因
int_features <- SelectIntegrationFeatures(object.list = ECs_list)
# 选择合并的anchor特征
int_anchors <- FindIntegrationAnchors(object.list = ECs_list, anchor.features = int_features)

# 根据anchor合并
ECs_combined <- IntegrateData(anchorset = int_anchors)

DefaultAssay(ECs_combined) <- "integrated"
rm("ECs_list", "int_features", "int_anchors")

multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),human_coronary)

multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),CA_dataset1)

multi_featureplot(c("TNFRSF11B","ACTA2","CNN1","LUM"),CA_dataset2)

genes <- c("LGALS3","CD68","KLF4","CLDN5","VWF","ACTA2")
multi_featureplot(genes,CA_dataset2)

Dotplot(genes,CA_dataset2) # 2>5

multi_featureplot(genes,human_coronary)

Dotplot(genes,human_coronary)

差异基因

annoation

cl4_marker <- FindMarkers(ds2,ident.1 = 4,logfc.threshold = 0.5, min.diff.pct = 0.3)

  |                                                  | 0 % ~calculating  
  |++                                                | 3 % ~00s          
  |++++                                              | 6 % ~00s          
  |+++++                                             | 10% ~00s          
  |+++++++                                           | 13% ~00s          
  |+++++++++                                         | 16% ~00s          
  |++++++++++                                        | 19% ~00s          
  |++++++++++++                                      | 23% ~00s          
  |+++++++++++++                                     | 26% ~00s          
  |+++++++++++++++                                   | 29% ~00s          
  |+++++++++++++++++                                 | 32% ~00s          
  |++++++++++++++++++                                | 35% ~00s          
  |++++++++++++++++++++                              | 39% ~00s          
  |+++++++++++++++++++++                             | 42% ~00s          
  |+++++++++++++++++++++++                           | 45% ~00s          
  |+++++++++++++++++++++++++                         | 48% ~00s          
  |++++++++++++++++++++++++++                        | 52% ~00s          
  |++++++++++++++++++++++++++++                      | 55% ~00s          
  |++++++++++++++++++++++++++++++                    | 58% ~00s          
  |+++++++++++++++++++++++++++++++                   | 61% ~00s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~00s          
  |++++++++++++++++++++++++++++++++++                | 68% ~00s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~00s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~00s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s